The archaeological and historical record allows us to study the patterns of economic inequalities associated with sharply contrasting institutions such as communal property in societies without states, private property among small holders, and slavery, in economies relying on radically different technologies, based for example on human energy alone, other animal power, and carbon-based power. But the price of using prehistoric and historical data to expand the range of institutions and technologies under study is a substantial and typically unknown level of uncertainty and unrepresentativeness in the resulting estimates. We provide methods and code that quantify the degree of uncertainty, an approach that we term BRIDGE (Bayesian-Resampling and Informed Priors with Data-driven Gini Estimation). We transform raw wealth data from 431 sites and dates into probability distributions representing likely levels of inequality that (insofar as possible) are representative of the underlying population (e.g., rectifying biases arising from small or nonrandom samples and the frequent absence of data on those entirely without wealth) and are comparable across differing asset types (e.g., burial goods, dwelling area, storage area, land cultivated), ownership units (individual or household), and scale (from villages to nations). These distributions are based on the raw data along with independent information that allows us to infer biases and levels of uncertainty. We also account for the propagation of uncertainty through every stage of the data generation and estimation process. We find that widely used methods (e.g., relying solely on conventional bootstrapping) provide misleading and for the most part substantially underestimated measures of uncertainty.
Fochesato et al. (2026) studied this question.